McMorran, BenCox, CourtneyMoffitt, MichaelHeckman, Amelia2022-07-122022-07-122022https://hdl.handle.net/1794/27326In recent years, collegiate sports have started turning to data analysis to assist in improving performance and training tactics. There are many opportunities in utilizing data-driven – or “hyperquantified” –approaches, such as talent identification, injury reduction, in-game decision making, and increasing profits. Many universities and professional organizations utilize models to predict success. While there are many benefits, there is less emphasis on the broader wellbeing of the athlete—which “success” in this context does not include. This thesis investigated one specific example of creating a predictive model of success at the University of Oregon as well as the issues that arise from using such a model. Three ethical implications that arise from hyperquantifying athletes discussed in this thesis include data reliability, data security, and athlete autonomy. Further research is recommended into how athlete wellbeing can be emphasized at the collegiate sport levels.en-USCC BY-NC-ND 4.0SportsTrack and fieldHyperquantificationAutonomyPythonThesis/Dissertation000000030661142X